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Deep Continual Learning in the Foundation Model Era (Dagstuhl Seminar 25432)

Authors: Christopher Kanan, Martin Mundt, Tinne Tuytelaars, Joost van de Weijer, and Timm Felix Hess

Published in: Dagstuhl Reports, Volume 15, Issue 10 (2026)


Abstract
This report documents the program and the outcomes of Dagstuhl Seminar 25432 "Deep Continual Learning in the Foundation Model Era". This seminar brought together 23 researchers to discuss research at the intersection of continual learning and foundation models. The discussion centered on the major challenges arising from continual training of foundation models, including the need for new benchmarks, new opportunities for memory-based continual learning, emerging application domains, and the development of efficient metrics to quantify forgetting of foundation model knowledge. In addition, the report contains a summery of the talks of the partipants.

Cite as

Christopher Kanan, Martin Mundt, Tinne Tuytelaars, Joost van de Weijer, and Timm Felix Hess. Deep Continual Learning in the Foundation Model Era (Dagstuhl Seminar 25432). In Dagstuhl Reports, Volume 15, Issue 10, pp. 119-134, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)


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@Article{kanan_et_al:DagRep.15.10.119,
  author =	{Kanan, Christopher and Mundt, Martin and Tuytelaars, Tinne and van de Weijer, Joost and Hess, Timm Felix},
  title =	{{Deep Continual Learning in the Foundation Model Era (Dagstuhl Seminar 25432)}},
  pages =	{119--134},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2026},
  volume =	{15},
  number =	{10},
  editor =	{Kanan, Christopher and Mundt, Martin and Tuytelaars, Tinne and van de Weijer, Joost and Hess, Timm Felix},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.15.10.119},
  URN =		{urn:nbn:de:0030-drops-254123},
  doi =		{10.4230/DagRep.15.10.119},
  annote =	{Keywords: continual learning, deep learning, foundation models}
}
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